Redefining the Diagnostic Approach to Adrenal Insufficiency: Re-Assessment of Baseline and Cortisol Increment Cut-Offs with the 1 µg Synacthen Test
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background and Objectives: Adrenal insufficiency (AI) is an endocrine disorder characterized by inadequate cortisol production, leading to non-specific symptoms that delay diagnosis. The Low Dose Synacthen Test (LDST) is commonly used to evaluate adrenal function, but traditional cortisol cut-offs may not accurately reflect adrenal function in all patients. This study aims to identify baseline cortisol cut-offs to accurately rule in and out AI, reassess the value of cortisol increment during LDST, and evaluate the accuracy of 30 and 60 min cortisol measurements in diagnosing AI. Materials and Methods: We conducted a cross-sectional analysis of patients who underwent LDST at Farhat Hached University Hospital. Diagnostic accuracy of baseline cortisol levels and cortisol increment was assessed using ROC curve analysis to determine optimal cut-offs for predicting LDST outcomes. Results: Among 163 patients (mean age 42.9 years, 63% female), baseline cortisol ≤ 5.35 μg/dL had 100% specificity but 41.5% sensitivity for LDST failure. Conversely, baseline cortisol ≥ 12.4 μg/dL had 100% sensitivity with 45.9% specificity. Single measurements at 30 and 60 min correctly classified 92.64% and 93.87% of cases, respectively. ROC analysis of 30 and 60 min cortisol increments showed high diagnostic accuracy (AUC 0.923 and 0.914, respectively). The optimal cortisol increment cut-off was 6.35 μg/dL for ruling in AI (99% specificity). Conclusions: We propose a novel AI diagnostic algorithm based on a single 30 min cortisol measurement, complemented by revised baseline cortisol cut-offs and cortisol increment as additional criteria. This approach may enhance diagnostic accuracy and minimize unnecessary testing, warranting further clinical validation.
Source: Medicina
Authors: Taieb Ach, Rim Dhaffar, Asma Ammar, Aycha Ghachem, Imen Halloul, Wiem Saafi, Hamza El Fekih, Ghada Saad, Yosra Hasni, Monia Zaouali
Institutions: Hôpital Farhat Hached, University of Sousse
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W4412514211
